Journal ArticleDOI
Application of Linear Random Models to Four Annual Streamflow Series
TLDR
In this paper, a simple method for describing random time series is illustrated by application to the annual streamflow data of the St. Lawrence, the Missouri, the Neva, and the Niger rivers.Abstract:
A simple method for describing random time series is illustrated by application to the annual streamflow data of the St. Lawrence, the Missouri, the Neva, and the Niger rivers. The technique is illustrated for identifying the appropriate form of the general autoregressive-moving average model by use of the sample autocorrelation function of each series. The values of the parameters of the suggested model of each series are estimated and the results checked to suggest further modification of the model. The best model for each of the four samples showed a reduction in the variance value to 0.49, 0.64, 0.59, and 0.62 of the original variance with the use of one or two parameters. The use of the model for one year ahead forecasting on the Missouri River data is shown.read more
Citations
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Journal ArticleDOI
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
TL;DR: Developing a hydrological forecasting model based on past records is crucial to develop a water quality forecasting model that can be applied to the Yangtze River basin.
A comparison of performance of several artificial intelligence
TL;DR: Lin et al. as discussed by the authors developed a hydrological forecasting model based on past records, which is crucial to developing a water forecasting model. But the model is not suitable for forecasting the future.
Journal ArticleDOI
Hybrid neural network models for hydrologic time series forecasting
Ashu Jain,Avadhnam Madhav Kumar +1 more
TL;DR: The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts.
Journal ArticleDOI
Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques
TL;DR: In this paper, a crisp distributed support vectors regression (CDSVR) model was proposed for monthly streamflow prediction in comparison with four other models: autoregressive moving average (ARMA), K-nearest neighbors (KNN), artificial neural networks (ANNs), and crisp distributed artificial neural network (CDANN), where the fuzzy C-means clustering technique first split the flow data into three subsets (low, medium, and high levels) according to the magnitudes of the data, and then three single SVRs (or ANNs) were fitted to
Journal ArticleDOI
Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation
TL;DR: In the present paper a fractionally differenced autoregressive integrated moving average (FARIMA) model is considered and this approach allows the modeling of both short‐ and long‐term persistence in a time series.
References
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Journal ArticleDOI
Noah, Joseph, and Operational Hydrology
TL;DR: In this paper, a series of investigations on self-similar operational hydrology are presented, and the present paper introduces and summarizes the results of these studies. But, as a replacement for statistical hydrological models, selfsimilar models appear very promising, and they account particularly well for the remarkable empirical observations of Harold Edwin Hurst.
Journal ArticleDOI
Some Recent Advances in Forecasting and Control
George E. P. Box,G. M. Jenkins +1 more
TL;DR: An approach to the design of discrete feedforward and feedback control schemes, which are of great importance for example, in the chemical industry, is given and has a close link with the forecasting problems discussed there.
Journal ArticleDOI
Time series analysis
TL;DR: The serial correlation coefficient as mentioned in this paper measures the degree of redundancy of information yielded by each hydrologic event, which implies that statistical parameters computed from a sequence of events are less reliable than is indicated by the sequence length.
OtherDOI
Autocorrelation of rainfall and streamflow minimums
TL;DR: In this article, the effect of nonrandomness in hydrologic studies is investigated, and a measure of non-randomness is defined for generating processes in a time series.